In a multiple-input multiple-output frequency-division duplexing (MIMO-FDD) system, the user equipment (UE) sends the downlink channel state information (CSI) to the base station to report link status. Due to the complexity of MIMO systems, the overhead incurred in sending this information negatively affects the system bandwidth. Although this problem has been widely considered in the literature, prior work generally assumes an ideal feedback channel. In this paper, we introduce PRVNet, a neural network architecture inspired by variational autoencoders (VAE) to compress the CSI matrix before sending it back to the base station under noisy channel conditions. Moreover, we propose a customized loss function that best suits the special characteristics of the problem being addressed. We also introduce an additional regularization hyperparameter for the learning objective, which is crucial for achieving competitive performance. In addition, we provide an efficient way to tune this hyperparameter using KL-annealing. Experimental results show the proposed model outperforms the benchmark models including two deep learning-based models in a noise-free feedback channel assumption. In addition, the proposed model achieves an outstanding performance under different noise levels for additive white Gaussian noise feedback channels.
翻译:用户设备(UE)将下链通道状态信息(CSI)发送到基站以报告连接状态。由于MIMO系统的复杂性,发送这种信息的间接费用对系统带宽产生了负面影响。虽然这个问题在文献中得到了广泛考虑,但先前的工作一般都有一个理想的反馈渠道。在本文中,我们引入了由变异自动自动计算器(VAE)启发的神经网络结构PRVNet,以压缩CSI矩阵,然后在噪音频道条件下将其发送回基站。此外,我们提议了一个最符合所处理问题特点的定制损失功能。我们还为学习目标引入了额外的正规化超参数,这对于实现竞争性业绩至关重要。此外,我们提供了一种高效的方法,用KL-ananaling来调和这一超参数。实验结果显示,拟议的模型比基准模型(包括两个无噪音反馈频道假设中的深学习模型)要优于基准模型。此外,拟议的模型还实现了不同噪音水平下的高级反响度。